We evaluated our algorithm and compared against existing work on some popular image data sets namely ImageNet-10 , ImageNet-Dogs , STL-10 , CIFAR-10 , CIFAR100-20 .
For CIFAR100-20 , we used the 20 meta classes as the class labels while evaluating clustering. For STL-10 similar to earlier approaches PICA [1]} and GATCluster [2]}, we used both train and test splits for training and evaluation. Note that PICA also uses unlabelled data split of 100k points in STL-10 which we don't use. ImageNet-10 and ImageNet-Dogs are subsets of ImageNet and we only used train split for these two datasets [3]}. We used the same classes as [4]} for evaluating on ImageNet-10 and ImageNet-Dogs datasets. The dataset summary is given in Table REF . We evaluate for the cluster accuracy, NMI, ARI of the computed cluster assignments (see Appendix for details).
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